





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         BertTokenizerFast,
         BlipImageProcessor,
         GPTaTokenizer,
         InstructBlipProcessor,
         PreTrainedTokenizerFast,
     )

@require_vision
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =tempfile.mkdtemp()

         UpperCAmelCase_           =BlipImageProcessor()
         UpperCAmelCase_           =GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)
         UpperCAmelCase_           =BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"							)

         UpperCAmelCase_           =InstructBlipProcessor(_lowerCAmelCase		,	_lowerCAmelCase		,	_lowerCAmelCase							)

         processor.save_pretrained(self.tmpdirname							)



      def 						lowerCAmelCase__     (						self:    str		,	**_lowerCAmelCase:    str							)    ->     int:
         '''simple docstring'''

         return AutoProcessor.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							).tokenizer



      def 						lowerCAmelCase__     (						self:    Tuple		,	**_lowerCAmelCase:    Any							)    ->     Tuple:
         '''simple docstring'''

         return AutoProcessor.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							).image_processor



      def 						lowerCAmelCase__     (						self:    List[Any]		,	**_lowerCAmelCase:    List[str]							)    ->     List[str]:
         '''simple docstring'''

         return AutoProcessor.from_pretrained(self.tmpdirname		,	**_lowerCAmelCase							).qformer_tokenizer



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Union[str, Any]:
         '''simple docstring'''

         shutil.rmtree(self.tmpdirname							)



      def 						lowerCAmelCase__     (						self:    str							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =[np.random.randint(255		,	size=(3, 30, 400)		,	dtype=np.uinta							)]

         UpperCAmelCase_           =[Image.fromarray(np.moveaxis(_lowerCAmelCase		,	0		,	-1							)							) for x in image_inputs]

         return image_inputs



      def 						lowerCAmelCase__     (						self:    str							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=self.get_tokenizer()		,	image_processor=self.get_image_processor()		,	qformer_tokenizer=self.get_qformer_tokenizer()		,	)
         processor.save_pretrained(self.tmpdirname							)

         UpperCAmelCase_           =self.get_tokenizer(bos_token="(BOS)"		,	eos_token="(EOS)"							)
         UpperCAmelCase_           =self.get_image_processor(do_normalize=_lowerCAmelCase		,	padding_value=1.0							)

         UpperCAmelCase_           =InstructBlipProcessor.from_pretrained(
             self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=_lowerCAmelCase		,	padding_value=1.0							)

         self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer_add_kwargs.get_vocab()							)
         self.assertIsInstance(processor.tokenizer		,	_lowerCAmelCase							)

         self.assertEqual(processor.image_processor.to_json_string()		,	image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor		,	_lowerCAmelCase							)
         self.assertIsInstance(processor.qformer_tokenizer		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_qformer_tokenizer()

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase		,	qformer_tokenizer=_lowerCAmelCase							)

         UpperCAmelCase_           =self.prepare_image_inputs()

         UpperCAmelCase_           =image_processor(_lowerCAmelCase		,	return_tensors="np"							)
         UpperCAmelCase_           =processor(images=_lowerCAmelCase		,	return_tensors="np"							)

         for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()		,	input_processor[key].sum()		,	delta=1e-2							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_qformer_tokenizer()

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase		,	qformer_tokenizer=_lowerCAmelCase							)

         UpperCAmelCase_           ="lower newer"

         UpperCAmelCase_           =processor(text=_lowerCAmelCase							)

         UpperCAmelCase_           =tokenizer(_lowerCAmelCase		,	return_token_type_ids=_lowerCAmelCase							)
         UpperCAmelCase_           =qformer_tokenizer(_lowerCAmelCase		,	return_token_type_ids=_lowerCAmelCase							)

         for key in encoded_tokens.keys():
            self.assertListEqual(encoded_tokens[key]		,	encoded_processor[key]							)

         for key in encoded_tokens_qformer.keys():
            self.assertListEqual(encoded_tokens_qformer[key]		,	encoded_processor["qformer_" + key]							)



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_qformer_tokenizer()

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase		,	qformer_tokenizer=_lowerCAmelCase							)

         UpperCAmelCase_           ="lower newer"
         UpperCAmelCase_           =self.prepare_image_inputs()

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

         self.assertListEqual(
             list(inputs.keys()							)		,	["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]		,	)

         # test if it raises when no input is passed
         with pytest.raises(_lowerCAmelCase							):
            processor()



      def 						lowerCAmelCase__     (						self:    Any							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_qformer_tokenizer()

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase		,	qformer_tokenizer=_lowerCAmelCase							)

         UpperCAmelCase_           =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_           =processor.batch_decode(_lowerCAmelCase							)
         UpperCAmelCase_           =tokenizer.batch_decode(_lowerCAmelCase							)

         self.assertListEqual(_lowerCAmelCase		,	_lowerCAmelCase							)






      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =self.get_image_processor()
         UpperCAmelCase_           =self.get_tokenizer()
         UpperCAmelCase_           =self.get_qformer_tokenizer()

         UpperCAmelCase_           =InstructBlipProcessor(
             tokenizer=_lowerCAmelCase		,	image_processor=_lowerCAmelCase		,	qformer_tokenizer=_lowerCAmelCase							)

         UpperCAmelCase_           ="lower newer"
         UpperCAmelCase_           =self.prepare_image_inputs()

         UpperCAmelCase_           =processor(text=_lowerCAmelCase		,	images=_lowerCAmelCase							)

         self.assertListEqual(
             list(inputs.keys()							)		,	["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]		,	)






"""simple docstring"""



import os
from math import logaa


def 			_UpperCamelCase						(    _A = "base_exp.txt"   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = 0
    _UpperCAmelCase        = 0
    for i, line in enumerate(open(os.path.join(os.path.dirname(_A   )       ,   _A   )   )   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = list(map(_A       ,   line.split(""","""   )   )   )
        if x * logaa(_A   ) > largest:
            _UpperCAmelCase        = x * logaa(_A   )
            _UpperCAmelCase        = i + 1
    return result


if __name__ == "__main__":
 print(solution())